Pure and Applied Geophysics

, Volume 174, Issue 4, pp 1827–1844

Intraseasonal Variability of Summer Monsoon Rainfall and Droughts over Central India

  • Sourabh Shrivastava
  • Sarat C. Kar
  • Anu Rani Sharma
Article

DOI: 10.1007/s00024-017-1498-x

Cite this article as:
Shrivastava, S., Kar, S.C. & Sharma, A.R. Pure Appl. Geophys. (2017) 174: 1827. doi:10.1007/s00024-017-1498-x

Abstract

Rainfall over Madhya Pradesh (MP) in central India has large intra-seasonal variability causing droughts and floods in many years. In this study, rainfall variability in daily and monthly scale over central India has been examined using observed data. Consistency among various datasets such as rainfall, surface temperature, soil moisture and evapotranspiration has been examined. These parameters are from various different sources and critical for drought monitoring and prediction. It is found that during weak phases of monsoon, central India receives deficit rainfall with weaker monsoon circulation. This phase is characterized by an anticyclonic circulation at 850 hPa centered on MP. The EOF analysis of daily rainfall suggests that the two leading modes explain about 23–24% of rainfall variability in intraseasonal timescale. These two modes represent drought/flood conditions over MP. Relationship of weak phases of rainfall over central India with real-time multivariate (RMM) indices of Madden Julian Oscillation (MJO) has been examined. It is found that RMM-6, RMM-7, RMM-1 and RMM-2 describe the weak monsoon conditions over central India. However, frequency of drought occurrence over MP is more during RMM-7 phase. Surface temperature increases by about 0.5°–1° during weak phases of rainfall over this region. Soil moisture and evapotranspiration gradually reduce when rainfall reduces over the study region. Soil moisture and evapotranspiration anomalies have positive pattern during good rainfall events over central India and gradually reduce and become negative anomalies during weak phases.

Keywords

Rainfall Central India drought intraseasonal interannual MJO 

1 Introduction

Droughts cause significant loss to economy and damage to environment. Droughts cannot be prevented, but much can be done to reduce the impacts through preparedness, mitigation using better forecasting techniques. Droughts occur due to deficient precipitation and high evapotranspiration. Central part of India, also known as the core monsoon zone (CMZ), receives most of precipitation during summer monsoon season [June, July, August and September (JJAS)]. The CMZ region is considered representative for both the mean performance as well as for variability of the monsoon over India (Sinha et al. 2011). The population of the Indian subcontinent depends on cereal and pulse production. Central India is counted as one of the biggest suppliers of wheat, soybean, paddy, etc. The summer monsoon rainfall is also important for fulfilling the demands of drinking water and agriculture. Drought disasters often cause reduction in crop production or even maximum crop failure over central India. This region receives almost 95% rainfall during southwest monsoon (JJAS). However, this region is vulnerable to extreme events such as droughts and floods and experiences regular, spatially broad, and long-term droughts that cause the most severe losses to the agricultural economy (Sikka and Gadgil 1980; Rajeevan 2001; Shrivastava et al. 2016a).

Rainfall variability in monsoon months over India is the result of large-scale monsoon variability in seasonal to inter-annual timescales as well as intraseasonal oscillation. The Asian monsoon is affected by various factors like El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO) and the Indian Ocean Dipole (IOD). Impact of ENSO on the Indian summer monsoon rainfall has been studied by several researchers since long (Sikka and Gadgil 1980; Shukla 1987; Kar et al. 2001; Kar 2007). The IOD and the ENSO affect the Indian monsoon at interannual time scale (Ashok et al. 2001). The predictions of the Indian summer monsoon rainfall (ISMR) and role of SSTs during monsoon 2009 have examined by Acharya et al. (2011). Active (wet) and weak (dry) phases of rainfall have been examined in various studies such as Goswami and Mohan (2001), Gadgil and Joseph (2003), Goswami and Xavier (2003), Pabón and Dorado (2007) and Shrivastava et al. (2016b) and many others. The intraseasonal variability of rainfall is associated with northward propagating large-scale intraseasonal oscillation (ISO) with a 30- to 60-day period, which also controls the onset of southwest monsoon season over India (Yasunari 1979; Lau and Chan 1986; Kar et al. 1997; Gadgil 2003). These are also associated with eastward propagating Madden Julian Oscillations (MJO) as suggested by Kar et al. (1997). Using daily rainfall data and real-time multivariate MJO indices (RMM) of Wheeler and Weickmann (2001), the intra-seasonal variation of daily rainfall distribution over the Indian region associated with various phases of eastward propagating MJO was examined to understand the mechanism associating the MJO to the intraseasonal variability of the Indian monsoon (Pai et al. 2011).

An increase in temperature affects crops and crop yield. Previous studies (Sun et al. 2005; Lucas-Picher et al. 2011) have found more surface temperature over the Indian region during drought conditions. Soil moisture information is important to understand drought and other extreme events because soil moisture and rainfall have one to one relationship. Many researchers have used soil moisture data in their study to understand the climate variability (Sun et al. 2005; Miralles et al. 2012; Shrivastava et al. 2016b).

Madhya Pradesh (MP) is India’s second largest state with an area of 76.1 million acres. It is one of the poorest states despite being one of India’s largest and naturally capable states in India (MPRD GoI 2011). The cultivated area in MP is 372 lakh acre. Area under multiple crops is 130 lakh acre and irrigated area is 32.37% in Madhya Pradesh (Gupta 2013). The backbone of economy of MP is agriculture with 74.73% of the population is rural and involved in the agricultural and associated activities of agriculture. As much as 49% of the land area is cultivable. The main crops of Madhya Pradesh are wheat, paddy, jowar, gram, groundnut, soybean and cotton. The main soil types found in Madhya Pradesh are alluvial, deep black, medium black, shallow black, mixed red and black, mixed (red, yellow) and skeletal soils (AGERCMPCG 2013). Along with MP, another state in India (Chhattisgarh) form the previously combined state of Madhya Pradesh (MP). These two states are shown in Fig. 1.
Fig. 1

The geographic regions of India depicting its states. The study region, the central India, consisting of the states of Madhya Pradesh and Chhattisgarh is shown separately. Boxes drawn in the figure refers to regions used to compute correlation of rainfall of central India (Box-A) with other regions in India. Latitude and longitude bounds of the regions are provided in Table 1

There have been several studies on monsoon variability over the monsoon core zone. However, no studies exist on the rainfall variability over Madhya Pradesh to understand the drought occurrence and its mechanism using rainfall, temperature, soil moisture and evapotranspiration. Therefore, in this study, rainfall and its relationship with evapotranspiration, soil moisture, temperature and winds have been studied using various methods over Madhya Pradesh in central India in intraseasonal timescale. In the Sect. 2, various datasets used in this study have been discussed. In Sect. 3, interannual variability, intraseasonal variability, atmospheric circulation anomalies, regression with soil moisture and evapotranspiration are discussed. The last section, Sect. 4 summarizes the main results.

2 Data and Methodology

High-resolution observational and remote sensing datasets are available for precipitation, temperature, soil moisture and evapotranspiration. These ground and satellite observations help to understand ground conditions of these variables. The India Meteorological Department (IMD) is responsible for collection of observational precipitation and temperature in India. IMD-derived gridded datasets provide ground condition of precipitation and temperature and this high resolution gridded datasets developed by Pai et al. (2011) for precipitation and Shrivastava et al. (2008) for temperature. These data have been used in the present study.

Remotely sensed soil moisture data from European Space Agency (ESA) Climate Change Initiative (CCI) and evapotranspiration from moderate-resolution imaging spectroradiometer (MODIS) MOD16 global evapotranspiration (ET) product have been used in this study as a proxy of observation. ESA-CCI has made available soil moisture data from various sensors, i.e., SMMR, SSM/I, TMI, AMI-WS, ASCAT, AMSR-E, WindSat, AMSR2, etc. The data sets have been developed following procedure described in Hain et al. (2011), Parinussa et al. (2012), Liu et al. (2011, 2012), and Wagner et al. (2012) available at http://www.esasoilmoisture-cci.org/. The merged products present volumetric soil moisture (m3/m3) at a spatial resolution of 0.25° by 0.25°. MODIS MOD 16 global evapotranspiration datasets are regular 1-km2 land surface ET datasets at 8-day interval have been used in the present study.

Atmospheric wind data from the European Centre for Medium Range Weather Forecasts (ECMWF) Reanalysis-Interim (ERA-Interim, Dee et al. 2011) have been used for the same period. Global reanalysis data sets (such as the ERA-Interim) provide a long-term and consistent data sets useful for climate analysis and diagnostics. The ERA-Interim reanalysis uses a four-dimensional variational assimilation method and a high-resolution global model at T255 resolution. In this study, reanalysis winds data at 0.25° by 0.25° have been used. The real-time multivariate MJO indices (RMM1 and RMM2) of Wheeler and Hendon (2004) were used for defining the various phases of MJO. The data were obtained from http://www.bom.gov.au/bmrc/clfor/cfstaff/matw/maproom/RMM/.

3 Results and Discussion

3.1 Climatological Feature and Interannual Variability of Rainfall

Figure 2 shows the climatology of monthly mean rainfall over central India in June, July, August and seasonal (JJA) mean, respectively. During all the three monsoon months, east and south west of Madhya Pradesh (MP) receive higher amount of rainfall (10–16 mm day−1). However, eastern MP (Chhattisgarh and surrounding region) receives 10–12 mm day−1 rainfall in July, August and seasonal mean (JJA). These are high altitude regions that receive higher rainfall during JJA. The central India known as a core zone of monsoon receives good amount of rainfall (8–12 mm day−1). Rainfall received in July and August is the highest during monsoon season over the study region. In June, about 4–6 mm day−1 rainfall occurs and this amount affects the sowing time and growth of paddy and soybean crops. Interannual standard deviation of rainfall over MP region in June, July, August and seasonal (JJA) has been examined (figure not shown). High rainfall variability has been observed over the region during August as compared to June or July. Interannual variability of rainfall is highest (20–25 mm day−1) over west MP, whereas, variability over east MP (Chhattisgarh and surrounding region) 10–15 mm day−1.
Fig. 2

Monthly mean rainfall climatology (mm day−1) over the study region based on India Meteorological Department (IMD) data for the period 1982–2014 a June, b July, c August and d seasonal mean (JJA)

Figure 3 shows the monthly anomalies of rainfall over central India for June, July and August from 1982 to 2014. The same figure has also the seasonal mean (JJA) anomalies of rainfall. The range of anomalies for each month is from +6 to −8 mm day−1. To examine the variability of rainfall over central India, it is necessary to examine the spatial and temporal pattern of such variability. It is found that there are few months when rainfall anomalies during all 3 months are not in same phase over central India. It shows that monthly rainfall received in central India in these 3 months may vary due to different mechanism. Table 1 shows the correlation of monthly rainfall in JJA over central India with other regions in India. The latitude and longitude bounds for various regions are west (68°E–74°E and 20°N–30°N), south upper (72°E–82°E and 15°N–20°N), south lower (73°E–81°E and 8°N–15°N), north lower (74°E–82°E and 27°N–31°N), north upper (73°E–81°E and 31°N and 37°N), east (84°E–89°E and 17°N–28°N) and northeast India (89°E–98°E and 22°N–30°N). It is found that rainfall over MP is significantly correlated with north, west and south upper India. Central India has no significant correlation with east and south lower India. MP rainfall has negative and significant correlation with north-east India. Therefore, the mechanism of rainfall variability in these months over central India could be different with neighboring regions.
Fig. 3

Year-wise variation of monthly anomaly of rainfall (mm day−1) for June, July, August and JJA over the study region for the period 1982–2014

Table 1

Correlation of rainfall in June, July, August and JJA season over Central India with that of other regions in India. The regions are identified in Fig. 1

Regions

June

July

August

(B) West India

(68°E–74°E and 21°N–30°N)

0.243

0.456

0.369

(C) South Upper India

(72°E–82°E and 16°N–21°N)

0.480

0.395

−0.001

(D) South Lower India

(73°E–81°E and 8°N–16°N)

0.115

0.534

0.145

(E) North Lower India

(74°E–82°E and 26°N–30°N)

0.631

0.501

0.283

(F) North Upper India

(73°E–81°E and 30°N–37°N)

0.545

0.446

0.296

(G) East India

(82°E–89°E and 17°N–28°N)

0.607

0.029

0.273

(H) North East India

(89°E–98°E and 22°N–30°N)

−0.301

−0.669

−0.527

Statistically signicant (at 95% confidence interval) correlations are provided in bold letters

3.2 Monthly Composite Analysis

From the time series of monthly and seasonal rainfall anomaly shown in Fig. 2b, flood (drought) years were identified using more than (less than) +1 (−1) standard deviation rainfall anomaly at monthly scale. Table 2 shows the extreme flood and drought years over the region during this period. The time period used to calculate rainfall anomaly is from 1982 to 2014 but the present study is focused more on the recent period (from 2002 to 2011). The central India receives highest rainfall during August month but, it is seen that August has faced maximum deficit years during last decade than in June or July. Interannual variation in JJA seasonal rainfall anomaly is less than monthly rainfall anomaly. As a mean, seasonal (JJA) rainfall anomaly is able to capture the deficit and excess rainfall years. Using the time series analysis, it is found that 2002, 2007 and 2009 were deficit years in the study region. From the time series of daily rainfall anomaly, weak monsoon phases have been identified and are shown in Table 3. Several long weak phases (10 days or more) have been found during drought years. However, many weak phases are also observed during normal years. The weak phases play an important role in seasonal mean rainfall and they decide if the season has deficit or excess rainfall. It may be noted that if these weak phases occur during June, then they affect crop yield. From Table 3 it is also seen that even when there are no long weak phases, the seasonal mean rainfall is below normal. This shows that if in a given season, rainfall is consistently less than normal, in seasonal mean, it might turn out to be a deficit year. It may be noted here that in the Indian monsoon seasons, during some period, monsoon trough shifts northwards and runs close to foot hills of Himalayas, resulting in drastic reduction in rainfall over India outside the foot hills and southernmost Peninsula. This is termed as break phase of monsoon. As in June, monsoon onset occurs, monsoon breaks generally occur during July and August. In this study, weak phases of the monsoon (including weak onset phase and break phases) over central parts of India have been studied. Figure 4 shows the composite rainfall pattern (mm day−1) for each month for excess, deficit years and the difference to understand large-scale coherent rainfall during extreme rainfall and drought years. Figure 4a–c shows the composite rainfall during excess and Fig. 4d–f shows the composite rainfall during deficit years for June, July and August, respectively. In the difference plot (Fig. 4g–i), the region with statistically significant difference at 95% confidence interval are shaded. It is seen that western and north western parts of region get affected more during deficit years, whereas central and northern parts of the region have more wet areas during excess years.
Table 2

Excess and deficit rainfall years for Central India

Months

Deficit

Excess

June

1987, 1992, 1996, 2009, 2010, 2012, 2014

1990, 1994, 2001, 2008, 2011, 2013

July

1984, 1987, 1989, 2002, 2004, 2008

1986, 1994, 1997, 2001, 2005, 2013

August

1993, 1998, 1999, 2000, 2001, 2005, 2007, 2008, 2009, 2014

1982, 1984, 1994, 2004, 2006, 2012, 2013

Table 3

Weak monsoon phases over central India

Year

Weak phases

Observed seasonal mean rainfall

2002

01–17 July

21–31 July

  

Deficit

2003

29–31 July

19–21 August

04–08 August

 

Normal

2004

21–30 June

10–16 July

20–25 July

27–30 August

Normal

2005

15–21 June

19–23 July

07–15 August

23–31 August

Normal

2006

15–23 June

   

Excess

2007

18–26 July

28 July–01 August

10–20 August

 

Deficit

2008

14–24 July

18–25 August

  

Normal

2009

16–20 June

24–31 July

01–11 August

17–25 August

Deficit

2010

19–30 June

22–25 August

  

Deficit

2011

01–05 July

24–27 July

16–19 August

21–23 August

Excess

Fig. 4

Composites of excess rainfall years a June, b July and c August; same for deficient rainfall years d June, e July and f August. Difference of rainfall between excess and deficit rainfall years g June, h July and i August; Shaded regions in difference plots (gi) show that the differences are significant at 95% confidence interval

Figure 5a–c shows the composite of wind difference between excess and deficient years at 200 hPa. In June and July, the Tibetan High is shifted northwestward during excess rainfall years. An anomalous anticyclone is seen centered on 68°E and 38°N. A weak cyclonic anomaly is seen over south China and parts of East Asia which becomes stronger in July and moves southward in July during excess rainfall years over MP. Easterly wind anomalies are seen over peninsular India and the Indian Ocean in June which are seen only over the oceanic region in July. Wind differences between excess and deficit years in August are characteristically different than in June and July. In August, an anomalous cyclonic circulation is seen over Tibet whose longitudinal extend is from 70°E to 110°E. The anticyclone is shifted northward. There is no much differences in winds over most parts of India and the Indian Ocean in August between these two contrasting set of years, As it is discussed above, the central India gets more rainfall during August; so, it is more important for water resources and agriculture purpose. Figure 5d–f represents the composite of wind difference between excess and deficient years at 850 hPa. In all the months, wind differences are characterized by stronger cross-equatorial flow, stronger southwesterly winds and establishment of an anomalous cyclonic circulation over the Gangetic plains in excess rainfall years as compared to deficient years. In July, stronger south westerly wind plays important role and the cyclonic anomaly (25°N) is stronger than in June. In August, south westerly wind differences are less than other months. Therefore, weaker south westerly winds and anti-cyclonic anomaly at 850 hPa are found over central India during all the deficit rainfall months. It may be noted here that the wind differences discussed above for 850 and 200 hPa are statistically significant at 95% confidence level.
Fig. 5

Difference of composite winds (ms−1) between excess and deficient rainfall years at 2000 hPa for a June, b July and c August; at 850 hPa d June, e July and f August

3.3 Intra-seasonal Variability of Rainfall over MP

3.3.1 Composite Analysis of Weak Phases

Daily climatology of rainfall from June to August has been computed by averaging daily rainfall from 1982 to 2014 from IMD-derived gridded data (at 0.25° × 0.25°). Daily anomalies of rainfall have been calculated by difference between daily rainfall and daily climatological mean. Table 3 shows weak phases of rainfall (JJA) from 2002 to 2011 over the study region. Negative rainfall anomalies for five or more days have been counted as a weak phase of rainfall. It is found that 2004 is normal rainfall year but many weak phases of rainfall have been observed in this year. The year 2008 is normal season of rainfall but two long weak phases from 14 to 24 July and from 18 to 25 August are noticed. Therefore, one or two extreme rainfall events affect the seasonal mean rainfall and even if there are long weak phases, seasonal mean rainfall may become normal. Table 3 represents the seasonal mean of rainfall. 2002, 2007, 2009 and 2010 were deficit and 2006 and 2011 were excess years over central India. In this study, many long weak phases were found over the study region. Following the same method as for rainfall, daily anomalies of zonal and meridional components of winds at 850 hPa have been computed and analyzed as described below.

In 2002, two long weak phases with weaker south westerly wind anomalies were seen in July over MP. In the first weak phase, an anti-cyclonic circulation anomaly at 850 hPa over south India is noticed with 10–15 mm day−1 deficit of rainfall during 02–11 July. In the second weak phase, an anti-cyclonic anomaly at 850 hPa over south MP is noticed with 10–15 mm day−1 deficit of rainfall during 21–31 July. In both the cases, one cyclonic anomaly over Pakistan region is seen. The year 2003 (a normal rainfall year) has one weak phase over central India in August. Strong cyclonic anomaly near Andhra coast over the Bay of Bengal is noticed with weaker south westerly winds over central India. This weak phase brings 6–10 mm day−1 deficiency in rainfall. In 2004, two weak phases in June and July were identified over central India. Strong westerly and weaker south westerly at 850 hPa were seen during 21–30 June and 10–16 July. An anti-cyclonic anomaly over south of MP with 6–8 mm day−1 rainfall deficit during 21–30 June and an anti-cyclonic anomaly with 6–10 mm day−1 rainfall deficit over central MP is also seen during 10–16 July. In 2005, two weak phases were noticed in August. In the first weak phase, an anti-cyclone anomaly over central India and strong westerly anomaly over north India with 10–15 mm day−1 is seen during 07–15 August. In the second weak phase, an strong easterly anomaly over south India, north westerly anomaly over north India, and an anti-cyclonic anomaly at 850 hPa with negative rainfall anomaly of 8–10 mm day−1 over central India has been noticed during 23–31 August. The year 2006 was an excess rainfall year and no weak phase was identified. In 2007, two weak phases during July and August have been seen. In the first weak phase, strong easterly anomaly over south India, strong north westerly anomaly over north India and anti-cyclonic anomaly at 850 hPa with 15–20 mm day−1 deficiency of rainfall over central India is seen during 18–23 July. In the second weak phase, strong south westerly with anti-cyclone over Western Ghats and the Arabian Sea at 850 hPa with 10–15 mm day−1 deficiency of rainfall over central India has seen during 10–20 August. In 2008, two weak phases in July and August were seen. In the first weak phase, strong anti-cyclonic anomaly covers the east and central India and cyclonic anomaly near to equator (5°N) at 850 hPa with 10–15 mm day−1 deficiency in rainfall over central India has been seen during 14–24 July. In the second weak phase, a ridge over central India at 850 hPa with 8–10 mm day−1 deficiency in rainfall over central India is seen during 18–25 August. The year 2009 was a deficit year but only one long weak phase has been noticed. In this weak phase, an anti-cyclonic anomaly over central India, strong north westerly with 15–20 mm day−1 deficit rainfall has been noticed over central India.

Figure 6a shows the composite of all the weak phases of rainfall and 850 hPa winds from 2002 to 2011. It is noticed that the weak phases contribute to about 8–15 mm day−1 rainfall deficit in the central parts of MP. Over other parts of the study region, rainfall deficit is 3–8 mm day−1. Therefore, these weak monsoon phases are responsible for causing severe droughts in the study region. As these weak phases are quite long, they also contribute to significant deficit in seasonal mean rainfall anomaly. At the same time, it is seen that the Western Ghats region also gets less rainfall than normal. However, foothills of eastern Himalayas in Bihar and northeast parts of India have positive rainfall anomalies. The drought phases in MP are characterized by an anticyclonic circulation anomaly centered on central parts of MP. The southwesterly winds are weaker than normal over the Arabian Sea and peninsular India causing less moisture flux convergence over the Western Ghats and central India. The anomalous northwesterly winds in the Gangetic plain seen in Fig. 6a bring dry air from northwest into the study region causing further reduction in rainfall. Surface temperature during crop growing seasons is very important for crop growth and subsequent yield. Figure 6b shows the composite of daily mean surface temperature anomalies over central India during 2002–2011 using IMD temperature (at 1.0° × 1.0°) datasets. This composite during weak phases has been made using the dates in Table 3. It is seen that temperature during weak phases over central India is more by 1° as compared to normal period. The temperature anomalies over eastern and western parts is less than central Madhya Pradesh. Temperature anomalies over Telengana (south of MP) and east Rajasthan (northwest of MP) are more than 1° when MP faces drought conditions. As the central region is very important for crops during monsoon period, the increase in temperature supports the occurrence of agriculture droughts.
Fig. 6

a Composite of anomalies of winds at 850 hPa and rainfall and b composite of surface air temperature anomalies during break phases of rainfall over central India from 2002 to 2011

3.3.2 Empirical Orthogonal Function (EOF) Analysis

In Table 1, it is shown that rainfall over central India does not have significant correlation with neighboring regions except some northern parts of India. To further examine coherent spatial and temporal pattern of rainfall in the surrounding region of central India, an EOF analysis has been carried out using daily rainfall anomalies month-wise and daily JJA anomalies season-wise. Two leading modes (EOF-1 and EOF-2) have been found for June, July and August months. The first leading mode (EOF-1) for 3 months and seasonal JJA explains about 16.5, 18.9, 16.8 and 16.8%, respectively. The second leading mode (EOF-2) for 3 months and seasonal JJA explains about 7.6, 7.0, 8.1 and 6.9%, respectively. The spatial pattern and their principal component (PC) time series are shown in Fig. 7a, b for June, Fig. 7c, d for July, Fig. 8a, b for August and Fig. 8c, d for JJA. The figures have also the plots of the principal component (PC) time series June, July, August and JJA, respectively. The EOF-1 for all the months and JJA have two broad bands of negative loadings one across central parts of India and other along the west coast of India. The loadings in central India extends from west coast to the east coast. There is a small patch of positive loading over northeast India. The PC time series has variability in synoptic and intraseasonal timescale. Therefore, the EOF-1 has very large-scale structure and represents drought conditions over MP in intraseasonal timescale. It may also be noted that the composite picture of rainfall shown in Fig. 6a is very similar to the one obtained for EOF-1 pattern seen in Figs. 7 and 8. The EOF-2 for June does not have any well-organized pattern. For July, August and JJA, the EOF-2 patterns have two zones of positive and negative loadings. These bands cover whole of India in two parts: first one has positive loading over south India and second has negative loading over central and northern parts of India. The PC time series has variability in synoptic and sub-seasonal time scale as in EOF-1. The regression of these time series on to rainfall yield the drought patterns over ventral India including MP. Therefore, the most important and leading variability obtained from EOF analysis is the drought (or flood) over central India (including MP) in intraseasonal time scale.
Fig. 7

EOF analysis of rainfall anomalies (mm day−1). a Spatial pattern of EOF-1; b spatial pattern of EOF-2 for June; c spatial pattern of EOF-1; d spatial pattern of EOF-1 for July; and e, f principal component (PC) time series for EOF-1 and EOF-2 for June and July

Fig. 8

Same as Fig. 7, but for a, b August; c, d seasonal mean (JJA) and e, f principal component (PC) time series for August and JJA

3.3.3 Rainfall Anomalies and MJO Indices

As seen in the previous section, monsoon rainfall over central India exhibits strong intraseasonal variability causing droughts or floods. Madden Julian Oscillation (MJO) is one of the most influencing factors of the intraseasonal variability of the monsoon rainfall over India. Pai et al. (2011) using IMD daily gridded rainfall data and Wheeler–Hendon MJO indices, examined the intra-seasonal variation of daily rainfall distribution over India associated with various phases of eastward propagating MJO life cycle. They have found that during MJO phases of 1 and 2, formation of positive convective anomaly over the equatorial Indian cause break monsoon type rainfall distribution over India. As the MJO propagates eastwards to west equatorial Pacific through the maritime continent, a gradual northward shift of the convective activity over the Indian Ocean is observed. During phase 4, the northward propagating convective zone merges with monsoon trough and enhances rainfall activity over the region. During phases 5 and 6, the patterns are reversed compared to that during phases 1 and 2 and India experiences active monsoon conditions. During the subsequent phases (7 and 8), the convective anomaly patterns are very similar to that during phases 1 and 2. A general decrease in the rainfall is also observed over most parts of the country. However, it is not clear from the above study and similar past studies if the real-time MJO indices represent drought conditions over MP and if so, which phases of MJO need to be monitored or forecasted.

Relationship of weak phases of rainfall over central India with real-time multivariate (RMM) indices (Wheeler and Hendon 2004) at intra-seasonal time scale has been studied. Figure 9a–e shows the composite of weak phases over central India during different RMM phases. Maximum weak phases over central India occur during positive phases of RMM-6 and RMM-7 than RMM-1 or RMM-2. Longer weak phases of rainfall are seen during RMM-7 than other modes. In RMM phase 1, deficiency in rainfall (10–15 mm day−1) over central India is seen and excess rainfall (8–10 mm day−1) over north-eastern India is seen. In RMM phase 2, deficiency in rainfall (12–15 mm day−1) over central India is seen and excess rainfall (8–10 mm day−1) over north-eastern India is seen. In phase 6 and phase 7, whole of central India and surrounding regions have 10–15 mm day−1 deficiency in rainfall. Therefore, it is found that mostly during RMM phase 7, long weak phases of rainfall occur in the study region and this phase should be monitored very closely for drought monitoring and prediction.
Fig. 9

Composite of break phases during different RMM phases a RMM phase 1, b RMM phase 2, c RMM phase 6, d RMM phase 7 and e composite of all phases (1, 2, 6 and 7)

3.3.4 Soil Moisture and Evapotranspiration

Soil moisture and evapotranspiration over a region are important indicators of drought occurrence and crop growth/yield. To quantify the soil moisture variability over the domain of interest due to leading modes obtained from the EOF analysis, soil moisture anomalies have been regressed with the PC time series (PC-1) of the leading mode (EOF-1). The pattern of regressed soil moisture for July, August months and JJ season are shown in Fig. 10. The soil moisture data for June has not been considered in this study as the satellite data availability for this month over the study region was too less. Moreover, monsoon onset takes place in this month. Soil moisture variability in June is dominated by the delayed or early onset of the monsoon over the region. During July and August, Fig. 10 indicates clearly that soil moisture is affected directly as the rainfall over the region is reduced in intra-seasonal timescale for all the months and season as a whole. However, spatial pattern of regressed soil moistures in July and August are more close to rainfall pattern. The large bands of negative soil moisture anomalies over central India (especially in August and JJ) indicate the impact of rainfall to cause a drought during week phases of rainfall.
Fig. 10

Regressed surface soil moisture (SM) pattern with PC time series of rainfall for a July, b August and c July and August

Figure 11 shows the regression coefficients for the PC time series (PC-1) of rainfall and evapotranspiration. The evapotranspiration (ET) data are available every 8 days. Therefore, another EOF analysis was carried out converting the daily rainfall anomalies to 8-day anomalies. In this case, the first leading mode explains about 31% of the domain averaged variance. This mode has also a spatial pattern similar to the pattern obtained from the EOF analysis using daily data. The PC time series of these 8-day anomalies were regressed with the evapotranspiration data. In June, the bands of negative ET anomaly values shown in Fig. 11a cover entire central India. However, the bands of positive and negative values are seen in Fig. 11b–d for July, August and JJA, respectively, with the central India having negative ET values. Negative values represent less ET during weak phases of rainfall. Figure 11 shows the impact of rainfall on evapotranspiration in intraseasonal timescale. It may be noted that soil moisture and ET used in Figs. 10 and 11 are derived from satellite data. The consistency seen in intraseasonal variation of soil moisture, evapotranspiration and rainfall suggest that these variables can be used for drought monitoring purpose in central India (MP).
Fig. 11

Regressed evapotranspiration (ET) pattern with PC time series of rainfall for a June; b July; c August and d June, July and August (JJA)

4 Conclusion

Rainfall variability (in daily and monthly scale) in the context of drought occurrence over Madhya Pradesh in central India during summer monsoon has been studied using observed datasets. Mechanism of such rainfall variability has been examined so that an effective drought monitoring system can be developed for the study region. Consistency among various datasets such as rainfall, surface temperature, soil moisture and evapotranspiration have been examined. These parameters are from various different sources and critical for drought monitoring and prediction. Main conclusions of the present study are the following.

Composite analysis of winds 850 and 200 hPa during excess and deficient rainfall years show that the southwesterly winds are stronger and a cyclonic circulation is formed at 850 hPa over central India during excess rainfall months. The Tibetan anticyclone is shifted west and northwards from its normal position when the study region gets above normal rainfall. The opposite happens when the regions get below normal (deficit) rainfall. During weak phases of monsoon, central India receives deficit rainfall with weaker monsoon circulation. This phase is characterized by an anticyclonic circulation at 850 hPa centered on MP. The EOF analysis suggests that the two leading modes explain about 23–24% of rainfall variability in intraseasonal timescale. These two modes also represent large-scale monsoon variability with central India and Western Ghat regions having same phase and the north-east India having opposite phase. These two modes also represent drought conditions over MP.

Relationship of weak phases of rainfall over central India with real-time multivariate (RMM) indices of MJO has been examined. It is found that RMM-6, RMM-7, RMM-1 and RMM-2 describe the drought conditions over central India. However, frequency of drought occurrence over MP is more during RMM-7 phase. Therefore, a relationship between droughts over MP and the eastward propagating MJO mode has been established. Surface temperature increases by about 0.5°–1° during weak phases of rainfall over this region.

An attempt has been made to document relationship between remotely sensed soil moisture and evapotranspiration with meteorological conditions over central India. It is found that the soil moisture and evapotranspiration gradually reduce when rainfall reduces. Soil moisture and evapotranspiration anomalies have positive pattern during good rainfall events over central India and gradually reduce and become negative anomalies during weak phases.

Copyright information

© Springer International Publishing 2017

Authors and Affiliations

  • Sourabh Shrivastava
    • 1
    • 2
  • Sarat C. Kar
    • 1
  • Anu Rani Sharma
    • 2
  1. 1.National Centre for Medium Range Weather ForecastingNoidaIndia
  2. 2.TERI UniversityNew DelhiIndia

Personalised recommendations